AWS launches second infrastructure region in Canada

Amazon Web Services (AWS) launched its second infrastructure region in Canada, the company said in a release.

With the AWS Canada West (Calgary) Region, AWS noted it was the first major cloud services provider to open an infrastructure region in Western Canada.

Customers will also have access to advanced AWS technologies, including data analytics, security, machine learning (ML) and artificial intelligence (AI). AWS also released a new economic impact study highlighting that the company is planning to invest an estimated $17.9 billion in Canada through 2037 via the new AWS Canada West (Calgary) region and the existing AWS Canada (Central) region in Quebec.

“With the launch of the AWS Canada West (Calgary) region, customers and partners across Canada now have additional infrastructure to deploy applications with greater resilience, availability, and lower latency, while enabling more customers to innovate with advanced technologies like artificial intelligence to help fuel economic development across the country,” said Prasad Kalyanaraman, vice president of infrastructure services at AWS.

“Our government is committed to positioning our domestic industries for long-term growth and sustainability. The powerful digital infrastructure being established by Amazon Web Services near Calgary, Alberta will support Canadian developers, startups, large enterprises and academic institutions in their work by enabling access to AWS’s powerful advanced cloud technologies,” said François-Philippe Champagne, Canada’s minister of innovation, science and industry.

AWS’s active customers in Canada include Bell Canada, BlackBerry, Calgary Sports and Entertainment, CI Financial, the National Hockey League (NHL), Natural Resources Canada, Neo Financial, Nutrien, RBC, SECURE, Sun Life, TELUS and University of Calgary, among others.

With the addition of the new region in Canada, AWS now has 105 availability zones across 33 geographic regions, with plans to launch 12 more availability zones and four more AWS Regions in Malaysia, New Zealand, Thailand, and the AWS European Sovereign Cloud.

AWS Regions are composed of availability zones that place infrastructure in separate and distinct geographic locations. Availability zones are located far enough from each other to support customers’ business continuity, but near enough to provide low latency for high availability applications that use multiple availability zones, AWS said.

Persistent Announces Strategic Collaboration Agreement with AWS to Accelerate Generative AI Adoption

AWS chief Adam Selipsky talks generative AI, Amazon’s investment in Anthropic and cloud cost-cutting

Adam Selipsky is shepherding Amazon’s cloud unit at one of the most important moments in tech history.

Selipsky, the CEO of the company’s cloud computing unit AWS, has been behind the various generative AI offerings Amazon has rolled out over the past few months as it aims to compete with Microsoft and others in the growing AI arms race.

AWS is a leader in the cloud market and a deeply profitable business for Amazon. However, some of its growth has been slowing down the past few quarters, a problem attributed to customers cutting back on their spending due to challenges with the wider economy.

Amazon Web Services CEO Adam Selipsky speaks Nov. 30, 2022, at an event in Las Vegas.

Simultaneously, the business has been at the forefront of Amazon’s push into generative AI, which flooded the public consciousness last year with the release of OpenAI’s popular chatbot ChatGPT. During a speech held late November at an AWS conference in Las Vegas, Selipsky unveiled the company’s response (kind of) to ChatGPT — an AI assistant for businesses called Q.

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The Associated Press recently spoke with Selipsky about how companies are spending on cloud services, Amazon’s investment in the artificial intelligence startup Anthropic and the future of generative AI. The conversation has been edited for length and clarity.

Q: Companies have been cutting cloud spending this year. Is that still happening?

A: A lot of our customers over the past several quarters have been pursuing cost optimization. Since day one, we’ve said that AWS and the cloud are the place to do that. We’ve seen that a lot of customers have gotten far through that cost optimization. And we have other customers who are still in the middle of it. We’re further through it, but it’s not over yet.

We’re also still seeing a lot of customers investing. The companies who are going to win are the ones who are investing now — in uncertain economic times — when some others are hesitating in their overall investments. And we’re working with a lot of customers who are doing just that. We’re also seeing tremendous interest in our generative AI offerings.

Q: What’s your vision for generative AI?

A: We really think about three different layers of the generative AI stack.

At the bottom layer of the stack is the infrastructure required to to do generative AI. We have a very large Nvidia GPU-based business and have designed and delivered our own custom-designed chips, including our Trainium and Inferentia chips.

At the top layer of the stack is consuming applications that have been built using generative AI. And for that, we have a coding companion for developers.

Q: Speaking of models, there were reports that Amazon is building a large language model called Olympus. Is that something that we should expect to see soon?

A: You should definitely expect to see multiple iterations of Amazon’s first-party models, which are already out there today under the Titan brand. It goes back to the idea that there’s no one model to rule them all. We want multiple models with different use cases. And I expect that they will collectively be very capable and very powerful.

Q: Can you chat with me about Amazon’s investment in the artificial intelligence startup Anthropic? There are reports that Google, which is also backing Anthropic, is upping its investments. Some say this is becoming some sort of proxy war between Amazon and Google. Do you see it that way?

A: No, I don’t. We have a very close, very tight relationship with Anthropic that’s very beneficial to both companies. Anthropic has chosen Amazon as its primary cloud provider for its mission critical workloads. The majority of Anthropic workloads will run on AWS. Period.

Q: How does Amazon think about safeguards as its building this technology?

A: Responsible AI is incredibly important and something that Amazon has been taking very seriously. We have a number of principles for responsible AI that we’ve been public about. We’ve done things like create these cards for our services, which talk about the uses of the model, the intended use of the model, about how they were trained. We try to provide more transparency into how some of these AI services are constructed and what they’re used for.

We think that a lot of the solutions around responsible AI are going to need to be multilateral solutions. We need a collaboration between cloud industry leaders, folks like AWS, and those producing models, like Anthropic, as well as governments, academia and others. That’s why we’ve been so active at participating in responsible AI forums at the White House and in the U.K.

Q: Where do you see the AI race going next year?

A: I think you’re going to see a very rapid evolution and change. And that’s partially reflective of the fact that we are still so early in the evolution of generative AI. That’s why I think adaptability and flexibility are actually incredibly important advantages for customers. In order for them to succeed with their business objectives and to delight their customers, they’re going to need to be very flexible, agile and adaptable in how they evolve their use of generative AI.

In the latter half of the 20th century, TV watchers and moviegoers began to meet a long list of intelligent machines: "The Jetsons'" maid Rosie, B-9 from "Lost in Space," HAL 9000 from "2001: A Space Odyssey," the "Star Wars" saga's C-3PO and R2-D2, and dozens more.

Science fiction laid out some of the basics of artificial intelligence for people, not only by presenting humanoid robotic characters, but also by explaining in dramatized ways the extent to which AI could influence human existence.

In the real world, AI is not a new concept either, even though the recent boom of applications such as ChatGPT might make it seem so. In the 1950s, a generation of mathematicians and scientists explored AI and its applications in human life. However, technology had not developed enough to pave the way for these AI pioneers to put their theories to the test.

In today's era of big data, eight decades after Alan Turing conceptualized the idea of computers thinking and behaving like humans, AI is an intrinsic element of daily life—automating everyday tasks that otherwise consume large amounts of time. It has already increased efficiency and boosted performance in the banking, entertainment, technology, retail and marketing fields, among others, prompting the proliferation of jobs related to AI at all career levels in most industries.

Revelo looked at data from a 2022 McKinsey report to see which jobs were the top jobs in AI, examining only companies that have adopted AI in at least one function. The World Economic Forum projects that new jobs will arise and others will be ousted by the changes in tasks performed by humans and machines.

According to a PwC AI study cited by WEF, "any job losses from automation are likely to be broadly offset in the long run by new jobs created as a result of the larger and wealthier economy made possible by these new technologies." It's unclear how long that "long run" might be, but there are plenty of opportunities to get involved in the industry now.

The following analysis ranks jobs based on the percentage of companies that hired for each particular role. The data also looks at the same measurement, but only for "AI high-performers," those companies that are industry leaders in AI.

- Share of companies who say they have hired for this role in the past year:

-- 33% of AI high-performing companies

-- 31% of all other companies

Combining computer science and design skills, data architects analyze the data framework of an organization to develop and improve database models. They store and organize company information so it can be easily retrieved when needed. Data architects must be knowledgeable in SQL and Oracle database management systems and data visualization software. Because AI initiatives require effective company-wide architecture, data architects are crucial to the success of AI projects.

- Share of companies who say they have hired for this role in the past year:

-- 58% of AI high-performing companies

-- 27% of all other companies

Machine learning engineers create, develop, and test artificial intelligence programs, software, predictive models, and algorithms that help machines identify patterns and act autonomously. Their skillset must include scripting, programming, and data science and statistics expertise. These engineers are tasked with constantly improving the design of a company's machine learning, which is very complex and increasingly in demand.

- Share of companies who say they have hired for this role in the past year:

-- 60% of AI high-performing companies

-- 31% of all other companies

AI data scientists apply analytical and coding skills to examine data and gather the information needed to design custom-made solutions for organizations. They search for and present their findings and suggestions to stakeholders to help them make strategic, informed decisions. When they specialize in AI, data scientists feed information to programs to mimic and optimize existing processes while developing new strategies. Data scientists are in demand because, as AI technology continues to advance, the people in these roles help companies make smart decisions with data as well as automate business processes that improve efficiency and lower operational costs.

- Share of companies who say they have hired for this role in the past year:

-- 46% of AI high-performing companies

-- 37% of all other companies

Building data channels to route information properly from various sources is the primary responsibility of data engineers. They consolidate, tidy up, and structure raw data to make it accessible and understandable for business analysts. Just as companies need data scientists to collect and analyze the data, they need data engineers to turn it into a collection that AI models can use.

- Share of companies who say they have hired for this role in the past year:

-- 42% of AI high-performing companies

-- 40% of all other companies

Software engineers can choose from a wide range of career options; they can design and develop programs, such as computer games or network control systems. Their main duty is to generate and optimize business applications, which makes them highly sought-after tech professionals in most industries. Software engineers must have a strong knowledge of computer engineering and programming languages. AI companies utilize software engineers to not only build AI applications but also to maintain AI systems. Their insights are invaluable to the future of AI companies because the AI models they build help inform companies' decisions.

Data reporting by Wade Zhou. Additional writing by Kelly Glass. Story editing by Jeff Inglis. Copy editing by Kristen Wegrzyn. Photo selection by Ania Antecka.

This story originally appeared on Revelo and was produced and distributed in partnership with Stacker Studio.

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